Prof. Jiu Liu

Harbin Engineering University

Experince: Liu Jiu is a professor and master's supervisor in the Department of Law at the School of Humanities and Social Sciences, Harbin Engineering University. He holds a PhD in International Law from China University of Political Science and Law and a joint PhD from the University of California, Davis. His research interests include nuclear energy law and international law. Since July 2016, he has been employed at Harbin Engineering University, serving as a lecturer, associate professor, and later promoted to professor (with promotion synchronized with the completion of his project). He has led the National Social Science Fund Youth Project "Research on the Legislation of China's Nuclear Damage Compensation System" (2018-2023) and other projects such as the Heilongjiang Provincial Philosophy and Social Science Research Planning Project. In 2014, he went to study at the University of California, Davis (September 2014 - September 2015), and in 2019, he participated in the International Atomic Energy Agency (IAEA) Regional Directors Meeting for Africa (Marrakesh, Morocco). He has published a monograph titled "Research on the Deposit Insurance System: Taking the American Experience and International Guidelines as a Starting Point" and an article titled "On the Construction of China's Nuclear Damage Liability System in the Context of the Atomic Energy Law" in "Shanghai Law Research". In 2021, he participated in the International Nuclear Law Training organized by the OECD Nuclear Energy Agency, and in June 2025, he represented the university to participate in the IAEA Nuclear Law Seminar and conducted research at Hainan Nuclear Power. He also serves as a council member of the International Economic Law Research Association of the Beijing Law Society (since 2016) and a member of the Harbin Municipal Committee of the Chinese People's Political Consultative Conference (2022 session).

Speech Title: The Development of Nuclear Industry in China Aiming at Carbon Peaking and Neutrality: Value, Risks and Response of Law

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Assoc. Prof. Aslina Binti Baharum

Sunway University

Experience: Ts. Dr Aslina Baharum is an Associate Professor and UX Researcher at the School of Engineering and Technology, Sunway University. Previously, she was a Senior Lecturer at Universiti Teknologi MARA (UiTM), and Universiti Malaysia Sabah (UMS). She also has industry experiences where she worked as an IT Officer for the Forest Research Institute of Malaysia (FRIM). She had experienced more than 20 years in the IT field.

She received a PhD in Visual Informatics (UKM), a Master Science degree in IT (UiTM) and graduated Bachelor of Science (Hons.) in E-Commerce from UMS. She is a member of the Young Scientists Network - Academy of Science Malaysia, Senior Member IEEE, and certified Professional Technologist from MBOT, and served as MBOT/MQA auditor.  

She won several medals in research and innovation showcases and was awarded several publication awards, teaching awards, Excellence Service award, and UMS Researchers Awards. She has co-authored and editor books, published several books of chapters (>20), technical papers in conferences and peer-reviewed and indexed journals (>60) papers. She also served as editor for several journals, scholarly contributed as a committee, editorial team and reviewers, and given several invited/ plenary talks at conferences.  

Her research interests include UX/UI, HCI/Interaction Design, Product & Service Design, Software Engineering & Mobile Development, Information Visualization & Analytics, Multimedia, ICT, IS and Entre/Technopreneurship. Her workshops and talks covered Entrepreneurship, Video/Image Editing, E-Commerce/Digital Marketing, AR/VR/MR/XR in STEM, Design Thinking and etc.

She is also a Certified Professional Entrepreneurial Educator, Executive Entrepreneurial Leaders and HRDF Professional Trainer. 

Speech  Title: When Algorithms Decide: Human Factors at the Heart of Digital Risk

Abstract:  As digital societies increasingly rely on algorithmic systems to inform, automate, and even replace human decision-making, the nature of risk is undergoing a fundamental change. From AI-driven credit scoring and healthcare diagnostics to content moderation and public service delivery, algorithms now shape outcomes that directly affect human lives. Yet many of the most critical failures in digital systems do not stem from technical inaccuracies alone, but from overlooked human factors, misaligned mental models, cognitive overload, poor transparency, and misplaced trust. This keynote argues that digital risk is, at its core, a human problem before it is a technical one. Drawing from human–computer interaction, UX research, and human factors ergonomics, the talk reframes algorithmic risk through the lens of human cognition, behaviour, and decision-making. It examines how design choices, interfaces, feedback mechanisms, explainability, and interaction flow can either amplify or mitigate risk in AI-driven systems. Using real-world examples from AI-enabled fintech, healthcare, education, and digital platforms across the Asia–Pacific region, this keynote highlights how culturally unaware and poorly designed systems can erode trust, reduce adoption, and introduce systemic vulnerabilities. The talk further explores the role of human-centred and culturally adaptive design in strengthening resilience, accountability, and ethical governance in digital infrastructures. By bridging algorithmic intelligence with human insight, this keynote offers an interdisciplinary perspective for researchers, practitioners, and policymakers seeking to design risk-aware, trustworthy, and inclusive digital systems. Ultimately, it calls for a shift from technology-first solutions toward human-centred digital risk management, essential for sustaining digital societies in an era of accelerating complexity and uncertainty.

Prof. Yi-Zeng Hsieh

National Taiwan University of Science and Technology

Experience: Yi-Zeng Hsieh (Senior Member, IEEE) received the B.S., M.S., and Ph.D. degrees in computer science and information engineering from National Central University, Taoyuan City, Taiwan, in 2004, 2006, and 2012, respectively. He is currently a Professor with the Department of Electrical Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan. He has authored or coauthored more than 100 journal and refereed conference papers. His research interests include deep learning, machine learning, neural networks, pattern recognition, image processing, computer vision, and swarm intelligence. He is also a Senior Member of both the IEEE Consumer Technology Society and the IEEE Signal Processing Society. He was a Member of both IET Taipei Local Network and IET Japan Local Network. He was an Associate Editor for multiple IEEE Transactions. In 2023, he was recognized as an Outstanding Young Scholar by the IEEE Consumer Technology Society.

Speech  Title: From Forecasting to Autonomous Energy Orchestration: Transformer-Driven and Generative AI–Empowered Smart Power Dispatch for Intelligent Buildings

Abstract:  The accelerating impact of climate change and the sustained growth of electricity demand have made intelligent energy management in commercial buildings a critical challenge for modern smart cities. Office buildings, in particular, exhibit highly volatile short-term power consumption patterns due to complex interactions among environmental conditions, human activities, and the operation of large-scale equipment such as HVAC and chiller systems. In this keynote, we present a unified framework that bridges accurate time-series power forecasting and autonomous decision-making for power dispatch by integrating Transformer-based deep learning models with generative artificial intelligence.

We introduce a lightweight yet effective forecasting architecture, termed GCT (Gated CNN-Transformer), which combines Gated Residual Networks for temporal feature selection, convolutional neural networks for local spectral pattern extraction, and a Transformer encoder for long-range dependency modeling and sequence reconstruction. The proposed model achieves superior prediction accuracy with significantly fewer parameters compared to conventional RNN and LSTM baselines, enabling practical deployment on edge computing platforms.

Beyond prediction, this work demonstrates how generative AI can be elevated from a passive analysis tool to an active operational agent. By feeding short-term load forecasts into a large language model–based reasoning engine, the system can automatically generate interpretable and actionable power dispatch strategies under predefined operational constraints, effectively transforming building energy management from a reactive to a proactive and autonomous paradigm.

Through extensive experiments on public benchmarks and real-world office building datasets, as well as a fully implemented web-based and Dockerized deployment, this talk illustrates a new vision for closed-loop, AI-driven energy management systems. The presented approach highlights how the convergence of Transformers and generative AI can reshape the future of intelligent buildings, digital twins, and sustainable energy orchestration in smart infrastructures.